Research on Human Resource Allocation Model Based on SOM Neural Network

With the fierce competition of the enterprise market, the human resource allocation of enterprises will face multiple risks. This article takes the connotation of human resource configuration management as the research object and establishes the human resource configuration model through SOM neural network. And the model is trained, learned, and tested. What's more, it is applied to human resources management to adjust the allocation of human resources for the enterprise in a timely manner. It provides a detailed basis for proposing coping strategies and has a great application value.

Application of Neural Networks in Distribution of the Load in Cluster-Based Web Systems

Cloud computing systems revolutionized the Internet, and web systems in particular. Quality of service is the basis of resource configuration management in the cloud. Load balancing mechanisms are implemented in order to reduce costs and increase the quality of service. The usage of those methods with adaptive intelligent algorithms can deliver the highest quality of service. In this article, the method of load distribution using neural networks to estimate service times is presented. The discussed and conducted research and experiments include many approaches, among others, application of a single artificial neuron, different structures of the neural networks, and different inputs for the networks. The results of the experiments let us choose a solution that enables effective load distribution in the cloud. The best solution is also compared with other intelligent approaches and distribution methods often used in production systems.

The role of environmental regulation, industrial upgrading and resource allocation on foreign direct investment: Evidence from 276 Chinese cities

Environmental pollution is becoming more and more prevalent in China, accompanied by the excessive expansion of the country's foreign direct investment in the scale of resource-based industries. This article uses the panel data of 276 prefecture-level cities in China from 2003 to 2016 to estimate the impact of environmental regulation on foreign direct investment by employing the Spatial Durbin model. The empirical results show that: firstly, environmental regulation, and foreign direct investment have an obvious spatial correlation. Secondly, environmental regulation significantly inhibits foreign direct investment and has significant negative space spillover. Thirdly, non-eastern cities' environmental regulation has significantly greater inhibitory effects on foreign direct investment than eastern cities, and the key cities' environmental regulation has greater inhibitory effects than ordinary cities. Finally, from the perspective of industrial upgrading and resource configuration, environmental regulation has significantly promoted foreign direct investment and have significant negative space spillovers. Therefore, the reasonable use of environmental regulatory measures through industrial upgrading and resource configuration to attract clean, capital-intensive and technology-intensive enterprises and to achieve the effect of "decontamination and clean" for foreign-funded enterprises is critical.

Resource Configuration Efficiency and Influencing Factors of Elderly Care Services Based on a Data-Driven DEA-Tobit Approach

Efficient resource configuration is critical to providing sustainable aged care services. Empirical studies are conducted with a two-stage data-driven method of DEA-Tobit. First, the BCC model of data envelopment analysis is employed, to determine whether the values obtained from the three efficiency aspects (namely technical, pure technical, and scale) are situated in the frontiers of production, whether DEA is effective, and whether the problems of uneconomical scale or low technical level are available. Next, the causes of these problems are analyzed. Then, the regression analysis is performed using Tobit with the influencing factors including GDP, the number of institutions engaged in elderly care, the number of day service centers in the communities, the number of medical service institutions for the elderly, and the density of the elderly population. Known as a city with a severely aging population, Shanghai will be selected for a case study to propose countermeasures from the three dimensions of the elderly group, the elderly care industry, and the market environment.

Three-Way Interaction Effect Model: Moderating Effect of Resource Between Business Age and Host Performance

In the field of accommodation sharing, little attention has been paid to micro-entrepreneurship of hosts. Based on the signaling theory and the resource-based theory, we proposed a three-way interaction effect model to investigate the moderating effect of resource configuration (business size and host reputation) on the relationship between business age and host performance. A statistical analysis of the secondary panel data crawled from Airbnb.com was tested through the negative binomial model. The results shown that: (1) Business age is positively related to host performance; (2) the positive impact of business age on host performance is stronger for smaller size; host reputation has no significant moderating effect on the relationship between business age and host performance; (3) the joint consideration of business age, size, and host reputation has a three-way interaction effect on host performance. The positive impact of business age on host performance is strongest for hosts with smaller size and higher host reputation. These results are helpful to understand the micro-entrepreneurship performance of hosts in the field of accommodation sharing.

The role of environmental regulation, industrial upgrading and resource allocation on foreign direct investment: Evidence from 276 Chinese cities

Environmental pollution is becoming more and more prevalent in China, accompanied by the excessive expansion of the country's foreign direct investment in the scale of resource-based industries. This article uses the panel data of 276 prefecture-level cities in China from 2003 to 2016 to estimate the impact of environmental regulation on foreign direct investment by employing the Spatial Durbin model. The empirical results show that: firstly, environmental regulation, and foreign direct investment have an obvious spatial correlation. Secondly, environmental regulation significantly inhibits foreign direct investment and has significant negative space spillover. Thirdly, non-eastern cities' environmental regulation has significantly greater inhibitory effects on foreign direct investment than eastern cities, and the key cities' environmental regulation has greater inhibitory effects than ordinary cities. Finally, from the perspective of industrial upgrading and resource configuration, environmental regulation has significantly promoted foreign direct investment and have significant negative space spillovers. Therefore, the reasonable use of environmental regulatory measures through industrial upgrading and resource configuration to attract clean, capital-intensive and technology-intensive enterprises and to achieve the effect of "decontamination and clean" for foreign-funded enterprises is critical.

Evaluating cloud interactions with costs and SLAs

In this paper, we investigate how to improve the profits in cloud infrastructures by using price schemes and analyzing the user interactions with the cloud provider. For this purpose, we consider two different types of client behavior, namely regular and high-priority users. Regular users do not require a continuous service, and they can wait to be attended to. In contrast, high-priority users require a continuous service, e.g., a 24/7 service, and usually need an immediate answer to any request. A complete framework has been implemented, which includes a UML profile that allows us to define specific cloud scenarios and the automatic transformations to produce the code for the cloud simulations in the Simcan2Cloud simulator. The engine of Simcan2Cloud has also been modified by adding specific SLAs and price schemes. Finally, we present a thorough experimental study to analyze the performance results obtained from the simulations, thus making it possible to draw conclusions about how to improve the cloud profit for the cloud studied by adjusting the different parameters and resource configuration.

Scheduling Hardware-Accelerated Cloud Functions

This paper presents a Function-as-a-Service (FaaS) approach for deploying managed cloud functions onto heterogeneous cloud infrastructures. Current FaaS systems, such as AWS Lambda, allow domain-specific functionality, such as AI, HPC and image processing, to be deployed in the cloud while abstracting users from infrastructure and platform concerns. Existing approaches, however, use a single type of resource configuration to execute all function requests. In this paper, we present a novel FaaS approach that allows cloud functions to be effectively executed across heterogeneous compute resources, including hardware accelerators such as GPUs and FPGAs. We implement heterogeneous scheduling to tailor resource selection to each request, taking into account performance and cost concerns. In this way, our approach makes use of different processor types and quantities (e.g. 2 CPU cores), uniquely suited to handle different types of workload, potentially providing improved performance at a reduced cost. We validate our approach in three application domains: machine learning, bio-informatics, and physics, and target a hardware platform with a combined computational capacity of 24 FPGAs and 12 CPU cores. Compared to traditional FaaS, our approach achieves a cost improvement for non-uniform traffic of up to 8.9 times, while maintaining performance objectives.